# ThinkETL

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## Posts

- [Snowflake CI/CD with schemachange Using GitHub Actions](https://thinketl.com/snowflake-cicd-with-schemachange-using-github-actions/): This guide shows how to use schemachange and GitHub Actions to create a fully automated CI/CD pipeline for Snowflake deployments.
- [Snowflake schemachange: Automate Database Deployments](https://thinketl.com/snowflake-schemachange-automate-database-deployments/): Automate database deployments in Snowflake using schemachange, a CI/CD tool for managing schema migrations and version control.
- [AI_PARSE_DOCUMENT: Read Documents using SQL in Snowflake](https://thinketl.com/ai_parse_document-read-documents-using-sql-in-snowflake/): Snowflake’s AI_PARSE_DOCUMENT function makes it possible to read and extract text from PDFs, Word files, and other unstructured documents using just SQL.
- [Slowly Changing Dimensions (SCD) Implementation in Snowflake](https://thinketl.com/slowly-changing-dimensions-scd-implementation-in-snowflake/): Learn how to implement SCD Type 1, 2, and 3 in Snowflake with practical SQL examples for handling dimension changes.
- [Snowflake INFER_SCHEMA: Create Tables Using Schema Detection](https://thinketl.com/snowflake-infer_schema-create-tables-using-schema-detection/): Identifying table structures from raw files and create tables dynamically using INFER_SCHEMA function in Snowflake.
- [Snowflake Cortex AISQL Functions](https://thinketl.com/snowflake-cortex-aisql-functions/): Explore how Snowflake Cortex AISQL functions bring the power of generative AI directly into SQL with real-time scenarios.
- [Event Tables in Snowflake](https://thinketl.com/event-tables-in-snowflake/): Event Tables in Snowflake collect telemetry data (logs, traces, events) from components like stored procedures, UDFs.
- [Data Unloading using COPY INTO Command in Snowflake](https://thinketl.com/data-unloading-using-copy-into-command-in-snowflake/): The COPY INTO command in Snowflake enables unloading data from tables into internal or external stage locations in formats like CSV, JSON, or Parquet.
- [Snowflake Directory Tables: Query and Manage Staged Files](https://thinketl.com/snowflake-directory-tables-query-and-manage-staged-files/): Directory Tables in Snowflake provide a structured way to access file metadata in both internal and external stages.
- [Snowflake File URLs: Securely Access and Share Staged Files](https://thinketl.com/snowflake-file-urls-securely-access-and-share-staged-files/): Snowflake File URLs provides controlled and secure data access to the staged files without exposing the entire stage.
- [Data Load using COPY INTO Command in Snowflake](https://thinketl.com/data-load-using-copy-into-command-in-snowflake/): The COPY INTO command in Snowflake is a powerful tool for efficiently loading data from staged files into tables.
- [HOW TO: Make External API calls from Snowflake?](https://thinketl.com/how-to-make-external-api-calls-from-snowflake/): Explore how Snowflake offers a secure and efficient way to make external API calls directly within its environment.
- [RESULT_SCAN in Snowflake](https://thinketl.com/result_scan-in-snowflake/): RESULT_SCAN in Snowflake retrieves the results of a previously executed query and presents them as a virtual table allowing to run subsequent queries 
- [HOW TO: Delete Rows From a DataFrame in Snowflake Snowpark?](https://thinketl.com/delete-rows-from-a-snowflake-snowpark-dataframe/): Discover how to delete rows from a DataFrame in Snowpark using Table.update() and Session.sql() methods.
- [Introduction to Snowflake Notebooks](https://thinketl.com/introduction-to-snowflake-notebooks/): Snowflake Notebooks provide an interactive environment to write, execute, and document SQL and Python code in one interface.
- [HOW TO: Merge two DataFrames in Snowflake Snowpark?](https://thinketl.com/how-to-merge-two-dataframes-in-snowflake-snowpark/): Table.merge() method in Snowpark allows merging the contents of a Snowpark table object with a DataFrame source based on a specified join condition. 
- [HOW TO: Execute SQL Statements in Snowflake Snowpark?](https://thinketl.com/how-to-execute-sql-statements-in-snowflake-snowpark/): Snowpark allows direct execution of SQL statements using the session.sql() method in conjunction with collect() method.
- [DATEADD Function in Snowflake: Add or Subtract Time Intervals from Dates](https://thinketl.com/dateadd-function-in-snowflake-add-or-subtract-time-intervals-from-dates/): DATEADD Function in Snowflake calculates a date or timestamp value by adding a specified interval to a given date or timestamp. 
- [Snowflake Alerts: Schedule Conditional SQL Statements](https://thinketl.com/snowflake-alerts-schedule-conditional-sql-statements/): A Snowflake Alert allows scheduled execution of conditional SQL statements, enabling users to monitor specific events or thresholds within their environment. 
- [HOW TO: Send Email Notification in Snowflake?](https://thinketl.com/how-to-send-email-notification-in-snowflake/): Snowflake offers built-in stored procedures  SYSTEM$SEND_EMAIL and SYSTEM$SEND_SNOWFLAKE_NOTIFICATION for sending email notifications.
- [ASOF Join in Snowflake](https://thinketl.com/asof-join-in-snowflake/): ASOF JOIN in Snowflake matches each row in one table to the closest row in another table based on a specified condition, typically involving time series data.
- [MIN_BY and MAX_BY Functions in Snowflake](https://thinketl.com/min_by-and-max_by-functions-in-snowflake/): MIN_BY and MAX_BY are aggregate functions in Snowflake that calculate the minimum or maximum value of a column and return the value of another column in that row.
- [HOW TO: Drop a Column from a Snowpark DataFrame?](https://thinketl.com/how-to-drop-a-column-from-a-snowpark-dataframe/): Learn how to drop a column from a Snowflake Snowpark DataFrame using drop() method with examples.
- [HOW TO: Add a New Column to a Snowpark DataFrame?](https://thinketl.com/how-to-add-a-new-column-to-a-snowpark-dataframe/): Learn multiple ways to add a new column to a Snowpark DataFrame that includes adding a column with default value, based on a condition, and adding multiple columns with examples.
- [HOW TO: Remove Duplicates in a Snowpark DataFrame?](https://thinketl.com/how-to-remove-duplicates-in-a-snowpark-dataframe/): The dropDuplicates() method in Snowpark returns a new DataFrame with all duplicate rows removed.
- [CASE Statement in Snowflake Snowpark](https://thinketl.com/case-statement-in-snowflake-snowpark/): Learn how a CASE statement can be constructed in Snowflake Snowpark using when() and otherwise() methods.
- [INSERT and Multi-Table Inserts in Snowflake](https://thinketl.com/insert-and-multi-table-inserts-in-snowflake/): Learn how to perform Insert, Multi-Row Insert and Multi-Table Inserts in Snowflake using INSERT and INSERT ALL statements.
- [Common Table Expressions (CTEs) in Snowflake](https://thinketl.com/common-table-expressions-ctes-in-snowflake/): A Common Table Expression (CTE) in Snowflake is a named subquery used within the execution scope of the query in which it is specified. 
- [HOW TO: Parameterize Expression Transformation in Informatica Cloud (IICS) ?](https://thinketl.com/how-to-parameterize-expression-transformation-in-informatica-cloud-iics/): Expression transformations can be fully parameterized in Informatica Cloud mappings, allowing fields to be created at the mapping task level.
- [Creating Snowflake Iceberg tables using AWS Glue as Catalog](https://thinketl.com/creating-snowflake-iceberg-tables-using-aws-glue-as-catalog/): An Externally Managed Iceberg Table is a table where an external system such as AWS Glue manages both the table data and metadata. 
- [HOW TO: Create Snowflake Iceberg Tables?](https://thinketl.com/how-to-create-snowflake-iceberg-tables/): A Snowflake Iceberg table Snowflake employs the Apache Iceberg format for organizing and storing data within the Snowflake data warehouse environment. 
- [Date Functions in Informatica Cloud (IICS)](https://thinketl.com/date-functions-in-informatica-cloud-iics/): A comprehensive guide to the date/time functions in Informatica Cloud Data Integration (IICS)
- [Informatica Cloud (IICS) Functions Guide](https://thinketl.com/informatica-cloud-iics-functions-guide/): A comprehensive guide to the most commonly used string and conditional functions in Informatica Cloud Data Integration (IICS)
- [HOW TO: Run Informatica Cloud Taskflow with Run Using option?](https://thinketl.com/how-to-run-informatica-cloud-taskflow-with-run-using-option/): Learn how to run a taskflow using pre-defined sets of saved values called Taskflow Inputs with Run Using option.
- [UDFs in Snowflake Snowpark](https://thinketl.com/udfs-in-snowflake-snowpark/): Discover how to create User Defined Functions (UDFs) in Snowpark and register them in Snowflake.
- [Top Informatica Cloud Courses in Udemy](https://thinketl.com/top-informatica-cloud-courses-in-udemy/): A curated list of top Informatica Cloud courses available on Udemy covering various Informatica Service offerings.
- [HOW TO: Update a DataFrame in Snowflake Snowpark?](https://thinketl.com/how-to-update-a-dataframe-in-snowflake-snowpark/): Discover how to update contents of a DataFrame in Snowpark using Table.update() and Session.sql() methods.
- [Window Functions in Snowflake Snowpark](https://thinketl.com/window-functions-in-snowflake-snowpark/): Discover how to utilize Window Functions in Snowpark to calculate aggregated values across related rows efficiently.
- [IN Operator in Snowflake Snowpark](https://thinketl.com/in-operator-in-snowflake-snowpark/): Learn how to implement IN operator that allows you to specify multiple values in a WHERE clause to filter the data from Snowpark DataFrame.
- [Joins in Snowflake Snowpark](https://thinketl.com/joins-in-snowflake-snowpark/): Learn how to perform a join on the data of the current DataFrame with another DataFrame based on a list of columns in Snowpark.
- [GROUP BY in Snowflake Snowpark](https://thinketl.com/group-by-in-snowflake-snowpark/): Learn how to implement GROUP BY clause in Snowpark that helps in grouping of rows based on specified columns to perform aggregate calculations.
- [Aggregate Functions in Snowflake Snowpark](https://thinketl.com/aggregate-functions-in-snowflake-snowpark/): Aggregate functions perform a calculation on a set of values and return a single value, often used in conjunction with the GROUP BY clause.
- [HOW TO: COPY Data from CSV Files INTO Snowflake Table using Snowpark?](https://thinketl.com/how-to-copy-data-from-csv-files-into-snowflake-table-using-snowpark/): A comprehensive guide to copy data from CSV files into Snowflake tables using Snowpark
- [HOW TO: Write data into Snowflake from a Snowpark DataFrame?](https://thinketl.com/how-to-write-data-into-snowflake-from-a-snowpark-dataframe/): Learn how to write data into Snowflake and save as table from a Snowpark DataFrame.
- [HOW TO: Create and Read Data from Snowflake Snowpark DataFrames?](https://thinketl.com/how-to-create-dataframes-in-snowflake-snowpark/): DataFrame is a core abstraction in Snowpark representing a query in your chosen language which gets converted into SQL to execute your code in Snowflake.
- [Introduction to Snowflake Snowpark for Python](https://thinketl.com/introduction-to-snowflake-snowpark-for-python/): The Snowpark is an intuitive library that offers an API for querying and processing data at scale in Snowflake.
- [DATEDIFF Function in Snowflake: Calculate the difference between two dates](https://thinketl.com/datediff-function-in-snowflake/): The DATEDIFF function in Snowflake facilitates the calculation of the difference between two date, time, or timestamp values.
- [GREATEST and LEAST Functions in Snowflake](https://thinketl.com/greatest-and-least-functions-in-snowflake/): The GREATEST function in Snowflake retrieves the largest value whereas the LEAST function returns the smallest value from a list of expressions.
- [Snowflake Dynamic Tables: Change Data Capture Simplified](https://thinketl.com/change-data-capture-using-snowflake-dynamic-tables/): Snowflake Dynamic Tables track the changes in the query data specified and refresh the materialized results incrementally through an automated process.
- [QUALIFY in Snowflake: Filter Window Functions](https://thinketl.com/qualify-in-snowflake-filter-window-functions/): QUALIFY clause in a SELECT statement allows you to filter query results of Window functions within the same query. 
- [GROUP BY ALL in Snowflake](https://thinketl.com/group-by-all-in-snowflake/): The GROUP BY ALL clause is similar to GROUP BY clause except that it automatically include all non-aggregate items in the SELECT clause for grouping.
- [Rank Transformation in Informatica Cloud (IICS)](https://thinketl.com/rank-transformation-in-informatica-cloud-iics/): Rank Transformation helps in identifying largest or smallest set of values in a group of data by filtering the data and ranking the values.
- [SQL Transformation: Query Mode in Informatica Cloud (IICS)](https://thinketl.com/sql-transformation-query-mode-in-informatica-cloud/): A SQL Transformation can be used to process a SQL query either using a "Saved Query" or a User "Entered Query".
- [SQL Transformation: Call Stored Procedures in Informatica Cloud (IICS)](https://thinketl.com/sql-transformation-call-stored-procedures-in-informatica-cloud-iics/): SQL Transformation in Informatica Cloud is used to call a Stored Procedure or Function, or execute SQL queries midstream in a mapping pipeline.
- [User-Defined Functions (UDFs) in Informatica Cloud (IICS)](https://thinketl.com/user-defined-functions-udfs-in-informatica-cloud-iics/): User-defined functions allows you to create and store complex transformation logic using built-in functions and reuse them across mappings.
- [Mapplets in Informatica Cloud (IICS)](https://thinketl.com/mapplets-in-informatica-cloud-iics/): Mapplets are a powerful feature to build reusable transformation logic and use across multiple mappings in Informatica Cloud.
- [HOW TO: Add Currently Processed Filename field for Flat Files using Amazon S3 v2 connection in IICS?](https://thinketl.com/add-currently-processed-filename-field-for-s3-flat-files-in-iics/): Learn how to add Currently Processed Filename field for Flat Files using Amazon S3 v2 connection in Informatica Cloud.
- [Informatica Cloud Advanced Serverless](https://thinketl.com/informatica-cloud-advanced-serverless/): Advanced Serverless is a fully managed serverless deployment solution with no clusters or software to manage with high-performance and built-in elasticity.
- [Informatica Cloud Data Integration Elastic (CDI-E)](https://thinketl.com/informatica-cloud-data-integration-elastic-cdi-e/): Informatica Cloud Data Integration Elastic (CDI-E) enables you to process your jobs on advanced Spark serverless compute engine.
- [Tag-based Data Masking in Snowflake](https://thinketl.com/tag-based-data-masking-in-snowflake/): Snowflake combines best of tagging and data masking features to protect sensitive data which is referred to as Tag-based Data Masking
- [Tagging in Snowflake](https://thinketl.com/tagging-in-snowflake/): Tags in Snowflake can be applied on the database objects which lets you discover, monitor and protect sensitive data.
- [Snowflake SnowPro Core Certification Preparation Guide](https://thinketl.com/snowflake-snowpro-core-certification-preparation-guide/): A comprehensive list of all resources that help you in your Snowflake SnowPro Core Certification examination preparation.
- [Pushdown Optimization Preview in Informatica Cloud (IICS)](https://thinketl.com/pushdown-optimization-preview-in-informatica-cloud-iics/): Pushdown Optimization Preview helps in identifying the SQL to be executed and issues if any in the mapping that is preventing it from being configured for PDO.
- [Search Optimization Service in Snowflake](https://thinketl.com/search-optimization-service-in-snowflake/): Search Optimization Service is a query optimization service that improves query performance of selective point lookup queries.
- [Query Acceleration Service in Snowflake](https://thinketl.com/query-acceleration-service-in-snowflake/): Query Acceleration Service is a feature to improve the performance of huge queries by offloading portions of query processing to the additional compute resources.
- [Caching in Snowflake](https://thinketl.com/caching-in-snowflake/): Learn how Snowflake caching helps in improving the query performance by making data retrieval faster and more efficient.
- [Snowflake User-Defined Functions (UDFs)](https://thinketl.com/snowflake-user-defined-functions-udfs/): UDF is a reusable component defined by user to perform a specific task which can be called from a SQL statement. 
- [Snowflake Snowpipe REST API](https://thinketl.com/snowflake-snowpipe-rest-api/): Snowpipe REST API lets users define a list of files to ingest into Snowflake and fetch reports of the load history by making REST API calls.
- [Continuous Data Loading and Monitoring using Snowpipe](https://thinketl.com/continuous-data-loading-and-monitoring-using-snowpipe/): Learn how to load files from external storage using the Snowpipe and monitor the load status of the files in Snowflake.
- [Introduction to Snowflake Snowpipe on AWS](https://thinketl.com/introduction-to-snowflake-snowpipe-on-aws/): A step by step guide on automating continuous data loading into Snowflake through Snowpipe on AWS S3.
- [Execute multiple SQL statements in a single Snowflake API request](https://thinketl.com/execute-multiple-sql-statements-in-a-single-snowflake-api-request/): Learn how to submit an API request containing multiple statements to execute to the Snowflake SQL REST API using Postman.
- [Introduction to Snowflake SQL REST API using Postman](https://thinketl.com/introduction-to-snowflake-sql-rest-api-using-postman/): Snowflake SQL REST API allows users to interact with Snowflake through HTTP requests, making it easy to integrate with other systems.
- [HOW TO: Generate JWT Token for Snowflake Key Pair Authentication?](https://thinketl.com/how-to-generate-jwt-token-for-snowflake-key-pair-authentication/): Learn what JWT token is, how to generate it and pre-requisites to generate it for Snowflake Key Pair Authentication.
- [Key Pair Authentication in Snowflake](https://thinketl.com/key-pair-authentication-in-snowflake/): Snowflake supports Key Pair authentication which uses a combination of public-private key pair for enhanced security.
- [Caller’s and Owner’s Rights in Snowflake Stored Procedures](https://thinketl.com/callers-and-owners-rights-in-snowflake-stored-procedures/): The stored procedures in Snowflake runs either with caller’s or owner’s rights which defines the privileges with which the procedure executes.
- [Exceptions in Snowflake Stored Procedures](https://thinketl.com/exceptions-in-snowflake-stored-procedures/): Snowflake allows catching exceptions and their error information that occur in  stored procedures by defining exceptions.
- [RESULTSET in Snowflake Stored Procedures](https://thinketl.com/resultset-in-snowflake-stored-procedures/): Snowflake allows storing the result set of a SELECT statement and return them as output in the form a table using RESULTSET.
- [Cursors in Snowflake Stored Procedures](https://thinketl.com/cursors-in-snowflake-stored-procedures/): Cursors allows you to loop through a set of rows of a query result set and perform same set of defined actions for each row individually.
- [Looping in Snowflake Stored Procedures](https://thinketl.com/looping-in-snowflake-stored-procedures/): Loops in Snowflake Stored Procedures enables you to execute a set of statements until a particular condition is satisfied.
- [IF-ELSE, CASE Statements in Snowflake Stored Procedures](https://thinketl.com/if-else-case-statements-in-snowflake-stored-procedures/): IF ELSE, CASE statements in Snowflake Stored Procedures provides a way to execute a set of statements if a condition is met.
- [EXECUTE IMMEDIATE in Snowflake Stored Procedures](https://thinketl.com/execute-immediate-in-snowflake-stored-procedures/): EXECUTE IMMEDIATE command in Snowflake executes SQL statements present in form a character string and returns the result.
- [Variables in Snowflake Stored Procedure](https://thinketl.com/variables-in-snowflake-stored-procedure/): A Variable is a named object which holds a value of a specific data type whose value can change during the stored procedure execution. 
- [Introduction to Snowflake Stored Procedures](https://thinketl.com/introduction-to-snowflake-stored-procedures/): Stored procedures allow you to write procedural code that executes business logic by combining multiple SQL statements.
- [Row-Level Security using Row Access Policies in Snowflake](https://thinketl.com/row-level-security-using-row-access-policies-in-snowflake/): A Row Access Policy is a schema-level object that is assigned to a table or view and determines whether a given row in them can be viewed by a user.
- [Row-Level Security using Secure Views in Snowflake](https://thinketl.com/row-level-security-using-secure-views-in-snowflake/): Row-Level Security is a security mechanism that limits the records returned from a database table based on the permissions provided to the currently logged-in user.
- [HOW TO: Create Mapping Task in IICS using REST API?](https://thinketl.com/how-to-create-mapping-task-in-iics-using-rest-api/): Learn step-by-step procedure on how to create a mapping task in Informatica Cloud using mttask REST API resource.
- [HOW TO: Update Mapping Task in IICS using REST API?](https://thinketl.com/how-to-update-mapping-task-in-iics-using-rest-api/): Learn how to update mapping task using mttask REST API resource in Informatica Cloud and various use cases of updating mapping tasks using REST API.
- [HOW TO: Connect Snowflake from Informatica Cloud (IICS)?](https://thinketl.com/how-to-connect-snowflake-from-informatica-cloud-iics/): Snowflake Data Cloud Connector allows connecting to Snowflake from IICS using which you can securely read/write data into Snowflake.
- [HOW TO: Find and Kill long running queries in Snowflake?](https://thinketl.com/how-to-find-and-kill-long-running-queries-in-snowflake/): Learn how to find and kill long running queries in Snowflake using the QUERY_HISTORY table functions available under Information Schema.
- [HOW TO: Get DDL of database objects in Snowflake?](https://thinketl.com/how-to-get-ddl-of-database-objects-in-snowflake/): Snowflake provides GET_DDL Function using which DDL of database objects like tables, views, procedures etc., can be extracted.
- [Top Snowflake Interview Questions](https://thinketl.com/top-snowflake-interview-questions/): A complete collection of top Snowflake Interview questions and answers
- [HOW TO: Parameterize Taskflows using Parameter Sets in IICS?](https://thinketl.com/parameterizing-taskflows-using-parameter-sets-in-iics/): A Parameter Set lets you parameterize a taskflow by assigning a parameter file to taskflow which provides values for  input parameters.
- [Snowflake Dynamic Data Masking](https://thinketl.com/snowflake-dynamic-data-masking/): Snowflake Dynamic Data Masking is a security feature that allows you to mask sensitive data in database tables, views, and query results in real-time. 
- [HOW TO: Remove Duplicates in Snowflake?](https://thinketl.com/how-to-remove-duplicates-in-snowflakes/): Learn different ways to remove duplicate records from a Snowflake table using SWAP WITH and INSERT OVERWRITE commands.
- [Partitioning target S3 files in Informatica Cloud (IICS)](https://thinketl.com/partitioning-target-s3-files-in-informatica-cloud-iics/): IICS supports creating multiple target S3 files by configuring a single target transformation using Distribution Column.
- [IICS Amazon S3 Connection - Temporary Credentials Duration](https://thinketl.com/iics-amazon-s3-connection-temporary-credentials-duration/): Learn how to fix ExpiredToken error while working with IICS Amazon S3 Connection using Temporary Credentials Duration.
- [IICS Amazon S3 v2 Connector – Authenticate via AssumeRole](https://thinketl.com/iics-amazon-s3-v2-connector-authenticate-via-assumerole/): Learn how to create Amazon S3 v2 Connection in Informatica Cloud using Temporary security credentials via AssumeRole
- [IICS Amazon S3 v2 Connector - IAM Authentication](https://thinketl.com/iics-amazon-s3-v2-connector-iam-authentication/): Learn how to create Amazon S3 v2 Connection in Informatica Cloud with IAM Authentication method using IAM Roles.
- [IICS Amazon S3 v2 Connector - Basic Authentication](https://thinketl.com/amazon-s3-v2-connector-basic-authentication-iics/): Learn how to create Amazon S3 v2 Connection in Informatica Cloud with Basic Authentication method using Access Keys.
- [Snowflake Resource Monitors](https://thinketl.com/snowflake-resource-monitors/): Snowflake Resource Monitors assist in cost management and prevent unforeseen credit usage caused by operating warehouses.

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- [Privacy Policy](https://thinketl.com/privacy-policy/): Privacy Policy for ThinkETL At ThinkETL, accessible from https://thinketl.com/, one of our main priorities is the privacy of our visitors. This Privacy Policy document contains types of information that is collected and recorded by ThinkETL and how we use it. If you have additional questions or require more information about our Privacy Policy, do not hesitate to contact us. This Privacy Policy applies only to our online activities and is valid for visitors to our website with regards to the information that they shared and/or collect in ThinkETL. This policy is not applicable to any information collected offline or via ... Read more


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